mirror of
https://github.com/bigscience-workshop/petals
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47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
import random
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import hivemind
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import pytest
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import torch
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import transformers
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from hivemind import P2PHandlerError
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from test_utils import *
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import petals
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from petals import DistributedBloomConfig
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from petals.bloom.from_pretrained import load_pretrained_block
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from petals.client.remote_sequential import RemoteTransformerBlock
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from petals.data_structures import UID_DELIMITER
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from petals.dht_utils import get_remote_module
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@pytest.mark.forked
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def test_remote_block_exact_match(atol_forward=1e-5, atol_inference=1e-3):
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dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
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config = DistributedBloomConfig.from_pretrained(MODEL_NAME)
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for block_index in random.sample(range(config.n_layer), 3):
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remote_block = get_remote_module(dht, f"{MODEL_NAME}{UID_DELIMITER}{block_index}", config)
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assert isinstance(remote_block, RemoteTransformerBlock)
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inputs = torch.randn(1, 8, config.hidden_size)
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outputs_forward = remote_block(inputs)
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outputs_inference = []
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with remote_block.inference_session(max_length=inputs.shape[1]) as sess:
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for i in range(inputs.shape[1]):
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outputs_inference.append(sess.step(inputs[:, i : i + 1, :]))
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# test that max length is respected
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with pytest.raises(ValueError, match=r"Maximum length exceeded") as exc_info:
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sess.step(inputs[:, -1:, :])
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assert "Maximum length exceeded" in repr(exc_info.value)
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outputs_inference = torch.cat(outputs_inference, dim=1)
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ref_block = load_pretrained_block(MODEL_NAME, block_index, torch_dtype=torch.float32)
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(outputs_local,) = ref_block(inputs)
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assert torch.allclose(outputs_local, outputs_forward, rtol=0, atol=atol_forward)
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assert torch.allclose(outputs_local, outputs_inference, rtol=0, atol=atol_inference)
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